We’re all busy integrating AI into our lives and the products we build. One thing that is clear is that AI adoption by users is not guaranteed; there’s emerging data that far more people use general models as a novelty rather than incorporating them into their day-to-day activities. This is not surprising and to be expected. The challenge is: how do you get the tools you’re building to be locked into users’ lives over the tools others are building?
Optimization vs. Adoption
For years, outside of those building new products, most UX professionals have been focused on optimizing mature systems. We’ve been laser-focused on refining workflows, polishing interfaces, and enhancing usability to deliver smoother, more intuitive user experiences. Optimization has undoubtedly played a crucial role in improving user satisfaction, engagement, and performance.
However, with the advent of AI, we are now facing fundamentally new workflows and outputs. AI doesn’t just enhance existing processes; it transforms them, often creating entirely new modalities of interaction. This requires a pivot of mindset from optimization to growing adoption—ensuring users embrace these new tools.
Delivering Success Through User-Centric Design
To successfully embed AI into our tools, I think we should prioritize user adoption. This involves designing experiences that are not just functional but deeply intuitive and engaging. We need to create environments where users feel confident and excited to explore these new technologies.
I’d argue that with AI, excitement is there. Confidence, however, is a composite of factors, including consistency, predictability, and successful outcomes. Of these, the latter is, I think, the most important for encouraging repeated use.
So, AI-powered tools need to deliver successful outcomes to encourage adoption. What success looks like will be different for every product, but ultimately, it matches the phrase, “I easily got the output I was expecting.”
Continuous feedback loops remain critical
Many of the adoption workflows are similar to optimization workflows. They are both iterative processes. Establishing continuous feedback loops ensures that we are attuned to user needs and challenges. Regularly soliciting and acting on user feedback allows us to iteratively improve the AI experiences we design, fostering greater trust and satisfaction.
A new priority but the same priority
Focusing on adoption is a new priority for many, but the true focus remains on those using the products we build. It’s just that the best thing we can do to support users as AI rolls through is to create systems that guarantee success in order to drive the adoption of this exciting new wave of technological advancement.